Can Music Classification be an Alternative for Brain Computer Interface Applications?

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Neurological studies on human brain show that, music is
an important tool that can be assessed for understanding the mechanism of the
brain. In this study, the availability of music classification for brain
computer interface systems was studied. Moreover, classification performances
of music tasks with other mental and motor tasks are evaluated. An experimental
study was carried out with three different subjects executing seven different
tasks. These tasks are; listening to music, relax, mental arithmetic, imagery right
hand movement, imagery left hand movement and the letter A imagination task.
Autoregressive (AR) parameters, Hjorth parameters, power spectral density (PSD)
values and PSD+ frequency characteristics were extracted as features from the
resulting EEG data. Their classification performances are tested with Support
Vector Machines (SVM), k-nearest neighborhood (k-NN) and Neural Network (ANN)
classifiers. By using AR parameters as features, the highest classification performances
were obtained as 100% SVM and 100% ANN. Classification performances were also
evaluated for different electrodes representing different sections of the brain
and it is observed that, C3 channel has the highest performance for music
tasks. As a result, we can conclude that music tasks affect different
frequencies in the brain, and that difference can be used in different brain
computer interface applications like medical, military or e-gaming applications.

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